On 2024-06-07 03:54:29, user Mohamed Hoballa wrote:

This preprint has been reviewed and published under the DOI: https://doi.org/10.30491/ja...

On 2024-06-06 17:27:56, user Prof. T. K. Wood wrote:

The first TA system found to inhibit phage was Hok/Sok in 1996 (that makes it seminal). So 26 years before retrons (your ref 47) and 25 years before ToxIN (your ref 48), Hok/Sok set the precedent of stopping phage by interpreting a phage process (transcription shutoff), rather than reacting to a specific phage protein. Curious as to why this discovery does not merit citation.

On 2024-06-06 14:21:54, user Coleen Murphy wrote:

Gainey et al. failed to use experimental and assay conditions specified in the Murphy lab's protocols and added extraneous, damaging steps; together, these resulted in the Hunter lab's failure not only to test TEI, but also their failure to replicate previous, well-established preference of C. elegans for PA14, learned avoidance of PA14 in the P0 generation, increased daf-7 expression in the ASI and ASJ, and intergenerational avoidance of PA14. To summarize, Hunter and colleagues have not in fact attempted to faithfully replicate our protocols in a manner that would have tested transgenerational epigenetic inheritance of learned pathogen avoidance, and therefore cannot make any claims about reproducibility.

On 2024-06-05 18:16:30, user Coleen Murphy wrote:

Point-by-point critique of Gainey et al. 2024:

Figure 1: <br /> 1. (A-C) It has been reported by many groups that PA14 is mildly attractive to C. elegans, that is, given a choice between PA14 and OP50, worms choose PA141,2. However, in almost every assay shown in this paper, the worms prefer OP50 over PA14 – that is, they are already avoiding PA14 - prior to training (naïve preference), which is odd. This suggests that the authors are not using conditions that are standard, either in PA14 or OP50 growth or in choice assays (see note about choice assay performance). This is a serious cause for concern that is independent of any training conditions. In fact, as far as we can see, in only one case (Fig. 1C, F1) did their experiments replicate the naïve choice results observed by other groups. <br /> 2. Choice assays: their “choice assays” involve putting 3-4x the recommended number of worms on a plate (up to 770 on a spot!), letting them roam for variable amounts of time (“30-60 minutes”) without trapping them (no azide or other paralytic used), and then putting them in a 4°C incubator (which does not immediately halt worm movement), then counting them. None of this follows our published choice assay protocols, or the standard chemotaxis assay protocol3–6. Putting more than 200 worms on a single plate can lead to altered choice because of crowding. In the absence of a paralytic, worms change their preference due to various factors, including adaptation; therefore, in this case, the worms’ first choice (which is what we measure in all our assays) is not being measured. They also count the worms by “aspirating” the worms off of the plate, which is not standard in any behavioral assays, as far as we know.<br /> 3. Table 2 and Figure 1: There are almost no true replicates, as in each experiment, at least one or more condition is changed. (For example, the authors only tested the PA14 we sent them in one replicate - Exp 3). <br /> 4. daf-7p::GFP imaging experiments (Fig. 1D, F, H) – Hunter and colleagues do not report seeing increased daf-7p::gfp expression in the P0 generation. Increased daf-7p::gfp expression after exposure to PA14 has been reported by multiple groups7, not just ours, and is usually not small or highly variable, as it is due to the combination of bacterial cues and P11 small RNA; if they cannot replicate this basic result, it suggests that something is seriously wrong with their protocols or technique, or their worms are very sick, even before trying to use our protocol to train worms. <br /> 5. Additionally, they do not report the expression of daf-7p::gfp in the ASJ neuron7, which is very strange, since we have been able to reliably replicate Meisel, et al.’s finding in the P0 generation. Therefore, it is not clear from which neuron the authors are quantifying daf-7p::gfp levels. <br /> 6. Instead of imaging and reporting fluorescence levels in individual neurons, the authors averaged fluorescence intensity/worm, which is explicitly not what we did or others have done, because different neurons in each worm can have different intensities – particularly if they are the ASI rather than ASJ neurons. <br /> 7. While we see modest decreases in fertility after PA14 training, the authors report severe decreases in fertility: about one fifth of normal egg production, and a severe developmental delay) in their F1 generation that we do not observe. Both facts indicate that their worms are very sick, even the worms that have not been exposed to PA14. If their worms are extremely sick, it might account for the small number of progeny, poor imaging results, and a developmental delay that shifted the training times. This could be a result of overbleaching, which causes developmental delays; the bleaching protocol described in Gainey et al. deviates from our published protocol. Additionally, they add Triton X100 to their final M9 wash, which is used (although at a higher concentration) to permeabilize embryos in other protocols. We are not aware of any bleaching protocols that include Triton in a wash step, and our lab certainly does not; this addition might also damage the progeny.

Figure 2 <br /> 1. P0 imaging data suggest that the daf-7p::gfp response to PA14 is not reproducible in their hands; again, this has nothing to do with our paper or protocols, but rather appears that they cannot replicate previous results in the field that precedes our work. <br /> 2. Does “25°C” mean that the worms were grown at or assayed at 25°C, or both? This high temperature is generally hard on the worms. <br /> 3. Technical note: it appears that instead of consistently picking fluorescent daf-7p::gfp animals, the authors “chunked” large groups of worms, resulting in populations of non-fluorescent animals in their experiments. <br /> 4. Scale of P0 and F1 are extremely different (due to sickness of the P0s?).

Figure 3 <br /> 1. Notes that panels A, C, and D are repeated from Figure 1.<br /> 2. The authors discuss “OP50 aversion” but this does not make sense, since both trained and untrained animals are placed on HGs after bleaching. <br /> 3. Their naïve in F1 is sometimes even lower than in the P0 (Fig. 3D).<br /> 4. There is no consistency in their results across replicates, within experiments, or across figures of the paper – not just the inability to see an F2 effect, but in their naïve chemotaxes, P0 trained choice indices, and F1 results; the authors claim that their F1 assays are reproducible, but only 3 out of the 9 assays in this figure show F1 learned avoidance. <br /> 5. In 3J, data that are not replicates, as they have been performed using different conditions, have been pooled. <br /> 6. Gainey et al. observe substantial variation in behavior between training plates (Figure 3, table 2, S2 annotated protocol), and incorrectly treat each training plate as a biological replicate, rather than a technical replicate. (Each training plate is seeded and grown in the same conditions, and worms from the same bleached population are added onto the plates, therefore these are not biological replicates but rather technical replicates; biological replicates require starting with different worm populations and carrying out the whole experiment independently.) In our hands, behavior from a set of training plates is always consistent. <br /> 7. Additionally, we note that the authors use the same population of worms for the choice assays and subsequently for bleaching, meaning that worms are held in liquid for an extended time before bleaching; this may cause worms additional stress which may interfere with behavior.

Figure 4 <br /> 1. OP50 growth conditions: this would only matter if the controls and experimentals were grown on different plate types, which is not the case (but if the authors are in fact putting the controls on different plates from experimentals, then the experiment is done incorrectly).

Figure 5 <br /> 1. We also found that sid-1 and sid-2 are required, but since their controls are inconsistent (Fig. 3) in the first place, it is hard to know how to interpret their data. <br /> 2. Other mutants (rde-1, hrde-1, sid-1, sid-2) – still show increased daf-7p::gfp in F1 – again, these data are hard to interpret since they do not show a wild-type control that worked here. This also has little bearing on our work since other training paradigms (e.g., 4- and 8-hour training that engages small RNA-independent pathways) also induce daf-7p::gfp. It is also unclear which neuron (ASI vs ASJ) they are imaging.

Discussion <br /> 1. daf-7p::gfp - Picking fluorescent worms or rollers is standard worm husbandry; it is not a “result” to say that they noticed that Rol can be lost – but it does indicate that they should have discarded any results that they obtained before noticing that the array might have been lost in the worms they assayed. The fact that they have brought this up more than once suggests that they are not using standard accepted practices to maintain transgenic lines. <br /> 2. Dennis Kim’s work on phenazine-induced avoidance has been oddly neglected in this work7. Kim’s group found that phenazine-1-carboxamide induces Pdaf-7::gfp expression in the ASJ neuron, which we see quite reliably in our assays as well. No Pdaf-7::gfp imaging of the ASJ neuron is presented in this work, suggesting that either the PA14 they grew also did not make phenazines, or their image analysis is unreliable. <br /> 3. They made a lot of changes to our protocol (temperatures, light/dark, etc). We cannot find in this paper a single example of an experiment that followed our protocol entirely. <br /> 4. The authors make a point of calling OP50 a pathogen, which is odd; C. elegans grown on OP50 typically live for 2-3 weeks. They cite Garigan et al. 20028, which showed that when worms get old (past 15 days) eventually the pharynx stops grinding up bacteria and the gut will start to fill up with OP50, and killing bacteria does slightly extend lifespan - but this is not an effect observed in young (Day 1) animals on the short timescales used in the experiments here. In any case, since both control and trained animals are grown on HG plates with OP50, it cannot explain the behavior of the control animals. <br /> 5. The authors also never replicate the “bias towards Pseudomonas in choice assays ((Ha et al., 2010; Lee et al., 2017; Moore et al., 2019)” – Those papers also used OP50 vs PA14 to demonstrate this bias towards Pseudomonas, so it is unclear how the author think that their failure to replicate this basic finding is somehow supportive of any of their arguments. It is more likely that there is something fundamentally wrong in their initial conditions that have prevented the replication of all other groups’ findings, not just ours. Moreover, in our experiments, other than the 24 hrs of training on PA14 vs OP50, our control and trained animals are always on the same plates. This argument makes no sense, unless the authors have introduced an additional variable of plating control worms on one kind of plate/bacteria and their trained animals on a different plate/bacteria (which we do not do). <br /> 6. It is unclear why the authors grew worms at different temperatures. 20°C is the standard temperature for worm growth and assays. <br /> 7. In our hands, naïve OP50-PA14 choice index is not significantly different between P0 (when NGM plates are used) and the subsequent generations (when HG plates are used). The survival assay correlates well with the idea that their worms are very sick, much sicker than we see in our assays, although the sparse intervals in both assays make it difficult to draw any conclusions – not possible to draw the conclusion that the bacteria are “more lethal” since they are trying to compare two lifespans from different labs etc. - but if they are, it might be due to their PA14 cultivation conditions or the health of their worms. But the fact that they see massive leaving and desiccation of worms, they might indeed be growing PA14 under much more pathogenic conditions. <br /> 8. The authors state: “Near the conclusion of these experiments, we received an updated protocol that included several clarifying edits and additional deviations from the published protocols (C. Murphy, Personal communication).”

We clarified our protocols, we didn’t “deviate” from them. This is a concerning way to present our email communications in which we tried to correct errors in their protocol and offer constructive advice; we even extended an invitation to Hunter to visit our lab to learn the assay. We are happy to provide these emails if necessary.

In order to help others, we continuously update our lab’s protocols to make clarifications that will help future users. Any note from the Murphy lab is an example of this type of updating. For example, later we made a new bacterial construct that used a Kan marker and constitutive promoter instead of an Ara inducible promoter and Carb marker to streamline experiments. This is not a deviation, it is a natural progression of the research in our lab and our practice of continuously improving our assays and updating protocols.

It is disingenuous for the authors to present our updates to our protocols as if we have “deviated” from them – in every instance, we gave the authors all of the information that we had available to us at the time. Our suggestions were made genuinely and in good faith, with the assumption that the authors wanted to get the assay working rather than using it to point out changes in our protocol.

Moreover, this statement corroborates our assertion that all or most of the data in this paper seem to have been generated using a protocol that differs significantly from our lab’s, as the bulk of their experiments appear to have been done before contacting us: “Incorporating these changes into our procedures did not reliably alter our results.” (no data shown)

1. “[T]his example of TEI is insufficiently robust for experimental investigation of the mechanisms of multigenerational inheritance” – The authors failed to test the fundamental requirement for transgenerational inheritance, that is, the expression of P11 sRNA by PA14, which only happens on plates at 25°C. Since they cite our subsequent papers where we first identified P11 sRNA as the key to TEI9, then our finding that the Cer1 retrotransposon is also required for P11-mediated TEI10 and then our finding that other Pseudomonas species use a similar small RNA to induce TEI11, they are definitely aware of this fact. Thus, it is not clear to us why they have not attempted to test P11 sRNA levels while searching for conditions that would replicate our findings. As a result, we can never know whether P11 sRNA was produced in any of the conditions that the authors tested in the experiments shown.

Together, Hunter and colleagues’ failure to replicate the basic naïve attraction to PA14 over OP50 demonstrated by other labs, their failure to replicate the P0 daf-7 expression published by other labs, and their failure to reliably replicate the P0 and F1 behaviors shown by other labs suggests to us that there are more basic concerns about their bacterial and C. elegans growth conditions, assay conditions, and assay techniques independent of any of the attempts to replicate the findings from our work.

References <br /> 1. Zhang, Y., Lu, H., and Bargmann, C.I. (2005). Pathogenic bacteria induce aversive olfactory learning in Caenorhabditis elegans. Nature 438, 179–184. https://doi.org/10.1038/nat....<br /> 2. Ha, H., Hendricks, M., Shen, Y., Gabel, C.V., Fang-Yen, C., Qin, Y., Colón-Ramos, D., Shen, K., Samuel, A.D.T., and Zhang, Y. (2010). Functional Organization of a Neural Network for Aversive Olfactory Learning in Caenorhabditis elegans. Neuron 68, 1173–1186. https://doi.org/10.1016/j.n....<br /> 3. Moore, R.S., Kaletsky, R., and Murphy, C.T. (2019). Piwi/PRG-1 Argonaute and TGF-β Mediate Transgenerational Learned Pathogenic Avoidance. Cell 177, 1827-1841.e12. https://doi.org/10.1016/j.c....<br /> 4. Moore, R.S., Kaletsky, R., and Murphy, C.T. (2021). Protocol for transgenerational learned pathogen avoidance behavior assays in Caenorhabditis elegans. STAR Protoc. 2, 100384. https://doi.org/10.1016/j.x....<br /> 5. Kauffman, A.L., Ashraf, J.M., Corces-Zimmerman, M.R., Landis, J.N., and Murphy, C.T. (2010). Insulin Signaling and Dietary Restriction Differentially Influence the Decline of Learning and Memory with Age. PLoS Biol. 8, e1000372. https://doi.org/10.1371/jou....<br /> 6. Kauffman, A., Parsons, L., Stein, G., Wills, A., Kaletsky, R., and Murphy, C. (2011). C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays. J. Vis. Exp., 2490. https://doi.org/10.3791/2490.<br /> 7. Meisel, J.D., Panda, O., Mahanti, P., Schroeder, F.C., and Kim, D.H. (2014). Chemosensation of Bacterial Secondary Metabolites Modulates Neuroendocrine Signaling and Behavior of C. elegans. Cell 159, 267–280. https://doi.org/10.1016/j.c....<br /> 8. Garigan, D., Hsu, A.-L., Fraser, A.G., Kamath, R.S., Ahringer, J., and Kenyon, C. (2002). Genetic analysis of tissue aging in Caenorhabditis elegans: a role for heat-shock factor and bacterial proliferation. Genetics 161, 1101–1112. https://doi.org/10.1093/gen....<br /> 9. Kaletsky, R., Moore, R.S., Vrla, G.D., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2020). C. elegans interprets bacterial non-coding RNAs to learn pathogenic avoidance. Nature 586, 445–451. https://doi.org/10.1038/s41....<br /> 10. Moore, R.S., Kaletsky, R., Lesnik, C., Cota, V., Blackman, E., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2021). The role of the Cer1 transposon in horizontal transfer of transgenerational memory. Cell 184, 4697-4712.e18. https://doi.org/10.1016/j.c....<br /> 11. Sengupta, T., St. Ange, J., Kaletsky, R., Moore, R.S., Seto, R.J., Marogi, J., Myhrvold, C., Gitai, Z., and Murphy, C.T. (2024). A natural bacterial pathogen of C. elegans uses a small RNA to induce transgenerational inheritance of learned avoidance. PLOS Genet. 20, e1011178. https://doi.org/10.1371/jou....

On 2024-06-06 12:01:12, user Elli wrote:

I am unable to see the suplementary tables the preprint refers to, where can I find these?

On 2024-06-06 12:00:30, user Elli wrote:

I am unable to see the supplementary tables the preprint refers to, where can I find these?

On 2024-06-06 09:51:33, user Asaf Levy wrote:

The paper is now published following peer review:<br /> https://academic.oup.com/is...<br /> Please cite this one.

Asaf Levy

On 2024-06-06 05:37:37, user Brian Junglen Jr wrote:

Can you please advise on the appropriate sample size required for a research study to conclusively determine the correlation between the spider gene and its impact on the snake's equilibrium?

On 2024-06-05 13:08:01, user Simon wrote:

This is an interesting analysis. I have two short comments/suggestions.

First, I wonder why authors made the more extreme Gly mutations for the binding site removal instead of the more conservative Ala mutations. Because of the special role of Glycine the quality of the input MSA is probably a lot worse for Gly instead of Ala mutations.

Second, I think authors should also report RMSD/pLDDT/PAE for the predicted structures itself and not just the ligand. Since AF3 performs the combined objective of structure prediction and docking mutation of the input has consequences for both objectives. It might be that it performs worse for docking because the overall quality of the structure prediction is reduced.

We've looked into the related problem of metal-protein interaction and there AF3 does a bit better to capture realistic physicochemical effects: https://x.com/simonduerr/st...

On 2024-06-05 07:04:18, user Si Hoffmann wrote:

The revised version of this article is published in Autophagy Reports.<br /> "Highlighting the hidden: monitoring the avidity-driven association of a fluorescent GABARAP tandem with microtubules in living cells"<br /> https://doi.org/10.1080/276...

On 2024-06-04 17:49:18, user phillip kyriakakis wrote:

Cool paper!

A few thoughts:

1) It would be great to see how this compares to the PhyB-PIF version<br /> 2) Blue light should activate PhyB/PhyA, it would be great to see different blue light doses to see how sensitive it is to blue light, not if it is sensitive to blue light. (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 3) I am not sure what biological replicates means. Where three independent experiments done, or just three biological replicates, one experiment? If a single experiment, this should be made explicit and perhaps written as N = 1.<br /> 4) PhyA could be written as PhyA-NT instead of delta. Delta implies it is a knock out or something. Peter Quail used the "NT" notation and that has been used a lot since, so it would be easy for others to follow. <br /> 5) What are the effects of far-red light, perhaps with and without blue light? (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 6) Would be nice to see blue and red systems multiplexed. Perhaps using DRE as in "Efficient photoactivatable Dre recombinase for cell type-specific spatiotemporal control of genome engineering in the mouse"

I am not suggesting these experiments or changes are needed to be published, but could improve the usefulness.

On 2024-06-04 07:43:30, user Wolfram Klapper wrote:

Excellent work! Congratulations! I wonder if you have checked the interdependence of HLA-I and EBV association and the effect on the microenvironment. Our own data show that TARC is a major driver and HLA-I is also an independent factor that associated with microenvironment features (see: https://onlinelibrary.wiley...<br /> Regards Wolfram

On 2024-06-04 02:45:41, user Adrian wrote:

This paper was already published in Nucleic Acid Research: https://academic.oup.com/na...

On 2024-06-03 17:49:15, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This paper addresses differences in bacterial, archaeal, and fungal microbiomes within certain body sites as a function of pregnancy status. Prior to this work, changes in the maternal bacterial, archaeal, and fungal microbiomes in body sites other than the vaginal cavity and gut were poorly understood. The authors characterize the oral, urinary, stool, and vaginal microbiomes, and the urinary metabolome of non-pregnant, as well as pre- and postpartum women. They conclude that the microbiome of the oral cavity quickly rebounds after birth, whereas the vaginal microbiome takes longer to return to its pre-pregnancy state. The authors also conclude that the archaeal content of the oral microbiome correlates strongly with pregnancy status. We feel that most of the conclusions put forth in the paper are well supported. However, we have concerns about conclusions involving archaeal microbiome differences, and we have suggestions for changes in data presentation that may improve clarity.

Major Comments

1. The authors do not consider possible confounding variables associated with pregnancy status. Hence, correlation of microbiome differences with pregnancy status might not indicate a direct causative effect. For example, diet and hospital visits are likely to differ between the three groups assessed in the study. The authors should discuss the potential for these and other confounding variables, and, if possible, incorporate relevant metadata in their analyses.
2. Figure 1B, Figure 2A and Figure 2B are informative summaries of microbiome content but they do not indicate variability between participants. We suggest including corresponding supplementary figures with equivalent plots for each individual person in each cohort.
3. The authors state that Methanobrevibacter smithii in the oral cavity is almost exclusively observed for pregnant women. This is most clearly indicated by Supplementary Figure 10. However, Figure 2C appears to show Methanobrevibacter smithii as the most abundant Methanobrevibacter species in the oral cavity for all groups, regardless of pregnancy status. The authors should clarify this apparent disparity.
4. The observation that Methanobrevibacter smithii is found in the oral cavity almost exclusively in pregnant women is one of the most striking parts of the paper. Hence, we recommend moving Supplementary Figure 10 into the main figures.
5. The data in Supplementary Figure 10 indicate raw sequence read counts. Given that read counts are a function of total read depth, we recommend including a more thorough analysis of these data, including a statistical test of significance for the differences observed between cohorts.
6. The authors describe “semi-quantitative” and “qualitative” approaches to detect archaeal taxa. The potential weaknesses of these approaches should be discussed in the Results section. The authors should also indicate which method was used for each of the datasets shown in the figure panels. What does “semi-quantitative” mean? And is it possible to draw conclusions from data generated by “semi-quantitative” or “qualitative” approaches?

Minor Comments

1. It would be helpful for non-experts to include definitions of some of the jargon used in the main body of the paper, such as Shannon diversity, richness, alpha diversity and beta diversity.
2. The presentation of metabolomic data in Figure 4B would be enhanced by (i) including a table in which the raw numbers are reported, and (ii) flipping the x-axis to provide a more intuitive view (highlights regulation postpartum).
3. The most abundant bacterial genera in each sample type (stool, oral, urine and vaginal samples) are more clearly communicated in Supplemental Figure 3 than in Figure 1B. This is especially true for stool data, where the microbiome composition is quite different from that in the other body sites, meaning that most taxa are reported as “other”. Hence, we suggest using Supplemental Figure 3 in the main body of the text rather than 1B.
4. In Figure 4A, glucose, succinic acid and fumaric acid are decreased in the postpartum state urine compared with pregnant state urine. Glucose is the starting energy molecule for glycolysis, and succinic acid and fumaric acid are intermediates in the TCA cycle. Can the authors comment on whether their decrease in postpartum women is a function of the catabolic energy status during lactation?
5. It would be helpful to include text that explains the characteristics of each CST grouping to help the reader better understand (i) how the samples were separated into these groups, and (ii) the significance of CST of the different groups.
6. Figure 3B. This representation is confusing, especially since there are lines with a single point on one side and multiple points on the other. The authors should provide a more detailed explanation of the data representation in the figure legend.
7. We recommend that the authors consider using color palettes that will make it easier for people with color blindness to distinguish between colors.
8. The representation of data in Figure 2C is difficult to follow. Can the authors use the same representation as Figure 2B?
9. Many of the references to supplementary figures are incorrect.

Suggestion for a Future Experiment

A further experiment may include collection of stool samples postpartum as well to look at multiple time-points postpartum. It would be particularly interesting to include later postpartum samples to observe how long it takes to return to the prepregnancy microbiome in each body site.

On 2024-04-29 13:34:46, user Oyinoluwa wrote:

Abstract & Introduction

The abstract is good overall and structured well. It gives a clear insight into the concern at hand and summarizes the key findings to show its significance. However, the research question is not clearly stated. This is something that can be elaborated on in the introductions, but it should also be stated briefly in the abstract. I understand that there is not a lot of research done on the effects of pregnancy to the female microbiota, but it is unclear as to what the research question is. This introduction gives a good background on the issue of pregnancy altering the microbiome. Below are my comments on what could be added or further explained.<br /> 1. Are you trying to determine which organisms are lost/recovered?<br /> 2. Is this study for a general understanding of the effects of pregnancy on women (pre/post birth)?<br /> 3. Are you looking at a specific family of bacteria, fungi or archea that has a meaningful change during both phases (pre- and post-maturation) to see their effects? <br /> 4. Are the women from diverse backgrounds (race)?<br /> 5. Was this study conducted in the same location?<br /> 6. Include more previous studies, if any, stating what others have claimed to be the reasons behind “side effects” of pregnancy <br /> 7. Include your own claims on what you would expect and not expect to see.

Figures and Tables

The table heading and description are good and easy to read. However, make sure to explain all abbreviations.<br /> Table 1. <br /> 1. What is the difference between an t-test and a Fishers exact t-test?<br /> 2. What does SS-days mean? <br /> 3. Also explain why each test was used for the p-value result.<br /> Figure 3. <br /> 1. I am not sure what these diagrams represent.<br /> 2. 3B- What is a vaginal community state type? How were they classified? It is stated that software clusters them, but what are the criteria?<br /> 3. 3A- What do the colors represent? Explain the unknown portion? <br /> General comments for figures<br /> Figures captions need to be clear, like in 1C ii) the title is “Rchress.” Is that observed ASVs? Include clear titles for your figures as well as axis labels. References to the figures from the results section show that the figures support the findings. You can include the reasoning behind choosing these graphical analyses. <br /> Methods, Materials & Analysis

1. In your sample and collection section, it is mentioned that the participants collected their own samples. Is there a reason for this? Why did a professional, specifically a doctor, not collect the samples? Was there a way to determine that the samples were collected and stored properly?
2. Does the study look at women across different races? Consider this please!
3. How was Lctobacillus classified to species level using only the V4 region? That is not possible, the full 16S sequence is required. https://www.nature.com/arti...
4. How did your account for chimeras and PCR errors before going onto further filtering steps?
5. Further explain your controls. It was mentioned that positive and negative controls were removed, but there is no mention of their identity.
6. What analysis was used to determine dominant and rare taxa? This was mentioned in the paper but was not included in the methods section.
7. This study used 16S rRNA analysis to determine species present as different body sites, when the sequencing step is conducted to determine the species what the sequencing coverage is. Is it complete? In addition, how many reads per sample? <br /> a. This was not stated at all in the paper, and it would be good to include it in the analysis portion.

Results, Discussion, Conclusion

In the limitations section, it is mentioned that the samples were not collected from the same women before and after pregnancy. Wouldn’t this affect the results you have presented? It is hard to say whether your results and conclusion support the claims which were made due to this aspect of sample collection. Although your results and conclusions are significant to the study, the validity is questionable.

On 2024-06-03 17:29:27, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This manuscript looks at the role of the TofI/R and QsmR regulators on virulence in Burkholderia glumae. Previous work suggested that TofI/R was the dominant regulator over QsmR in B. glumae quorum sensing. Here, the authors identify independent roles of TofI/R, distinct from QsmR, and suggest a regulatory effect of QsmR on TofI/R. Moreover, the authors conclude that the QsmR variant T50K substantially reduces B. glumae virulence. The conclusions regarding the hierarchy of TofI/R and QsmR are generally well-supported across multiple independent assays, although there are some inconsistencies between the RT-qPCR and the RNA-seq data. The conclusions regarding the importance of the genotype of qsmR rely heavily on control data that are not shown; in the absence of these data, it is impossible to draw a strong conclusion on the contribution of qsmR to virulence.

Major Comments

1. There are some inconsistencies between the RT-qPCR and the RNAseq data. While the RT-qPCR data indicate ~4-5 times higher toxJ transcripts in 411gr-6 vs 336-gr1, the transcriptomic data from Figure 10 show no difference between the two strains.This is similar for toxA, but the difference between the two isn’t as big. Could the authors provide an explanation for these apparent inconsistencies between experiments? It would also be helpful if the authors could indicate in Figure 10 the variability between replicates.
2. In Figure 9, the authors show that introducing qsmR from a virulent strain into an avirulent strain that produces the T50K variant of QsmR restores virulence. But the key control of introducing the variant qsmR is listed as “not shown”. Data for this control are critical to support the authors’ conclusions and should be included in the figure. We also recommend that the authors complement qsmR deletion mutants with qsmR (both T50K and wild-type) to test whether these can restore virulence phenotypes.
3. In Figure 9 A, C, and D, can the authors include a positive control to indicate the extent to which introducing qsmR restores virulence?

Minor Comments

1. Why and how were qRT-PCR data normalized to two housekeeping genes?
2. Can the authors include a brief discussion on the subset of virulence genes chosen for the RNA-seq?
3. It would be informative if the avirulent strain could be used as a control in Figure 2.
4. The method used by the authors to quantify disease severity does not appear to be quantitative and could be difficult to reproduce. Could the authors provide a quantitative assay to allow other groups to reproduce these experiments? Additionally, can the authors comment on whether or not samples were blinded before assessing disease severity, given the possibility for unconscious bias?
5. Could the authors clarify their use of statistical significance markers (Figures 2B and 2D).
6. Figure 10B might be better represented as a heatmap.
7. Figure 9B. For those outside the field, it would be helpful to describe in the text the phenotypic differences for infected virulent vs. infected nonvirulent vs. uninfected.
8. It would benefit the reader if there was a final model figure to tie everything together.

Suggestion for a Future Experiment

Are strain phenotypes specific to the species of rice being used in experiments, i.e., is the hypervirulent strain more of a generalist whereas the “wild-type” strain is a specific pathogen for this rice species?

On 2024-06-03 08:51:10, user Texugo wrote:

Can I have access to the raw data ?

On 2024-06-03 08:07:01, user Marjorie Guichard wrote:

A peer reviewed version is now available in Scientific Reports: https://www.nature.com/arti...

On 2024-05-31 20:17:09, user Guest wrote:

If all counts are assumed to be true positives and FDR = FP/(FP+TP), then isn't FDR = 0? I am not a biostats person, so just confused.

On 2024-05-31 18:55:39, user Julio Neto wrote:

Could you provide additional references that support the adverse outcomes of metaplasia in endothelial cells and the transformation of these cells to have connective and contractile properties? Also, I'd like to know if the relaxation of endothelium-dependent from isolated blood vessels is impaired in hypertensive animals. What are the basal blood pressure values of these awake animals? Are SOX-2 and KLF-4 transcription factors related to pluripotent-induced stem cells so, among these, how play a major role in determining endothelial drive instead of other cell types? (cardiac, for example). Preliminary data presented here could be from the transcriptome of single-cell RNA, which led to the sequence of the study. Lastly, I suggest reducing the range of graphs in Figure 6 (e.g., U46619 from 30 pM to 10 µM) to generate the best fit Hill sigmoid with more reliable efficacy and potency values. Congratulations and good luck!

Mark Johanson in this source as a main idea states that the messages employees send and receive on company work devices are always very accessible and monitored by their employers. Employees overall need to be very cautious when using their work devices for personal use. One key detail that Johanson mentions is that particular workplace platforms like Slack and Microsoft Teams often allow companies that use them to closely monitor their employees by allowing them to record and keep track of employees' activities and messages.

On 2024-05-31 09:07:32, user Klaus Harms wrote:

Article now published in Mol Microbiol: https://doi.org/10.1111/mmi...

On 2024-05-30 23:46:19, user Clashing_titans wrote:

Cogent, clear, well-written and an interesting piece to the Legionella effector story:<br /> Chadha et al should be proud of this work!

On 2024-05-30 19:17:48, user Sharath Tippur Narayana Iyenga wrote:

Hi,<br /> Interesting work for rapid separation and detection of bacteria from blood. However, the 2nd step of ''Selective cell lysis'' is used from the work which is already published and has a patent approval in progress. This original work has not been cited in this pre-print. This has to be added in the original paper before publishing. It is highly important to properly cite the original paper if their method is utilized in another work. The original paper citation details is below:

Narayana Iyengar S, Dietvorst J, Ferrer-Vilanova A, Guirado G, Muñoz-Berbel X, Russom A. Toward Rapid Detection of Viable Bacteria in Whole Blood for Early Sepsis Diagnostics and Susceptibility Testing. ACS Sens. 2021 Sep 24;6(9):3357-3366. doi: 10.1021/acssensors.1c01219. Epub 2021 Aug 19. PMID: 34410700; PMCID: PMC8477386.

On 2024-05-30 17:30:49, user Pawan wrote:

Where is the supplementary material of this paper?

On 2024-05-30 16:57:13, user sanalkumar rajendran wrote:

Dear Authors, this quite interesting and great effort to bring published HiChip work in one single platform. We, Riggi lab has published couple of articles in sarcoma models which includes HiChIP profiling from relevant cell lines, mesenchymal stem cells and specific oncogene knock-down condition. It would be nice to have those datasets also included in the Loop Catalogue. <br /> 1. Nature Communications volume 13, Article number: 2267 (2022). https://www.nature.com/arti...<br /> 2. SCIENCE ADVANCES, 31 Mar 2023, Vol 9, Issue 13, DOI: 10.1126/sciadv.abo3789<br /> Thanks

On 2024-05-30 16:51:25, user Djo Hasan wrote:

Dear authors of the article, entitled: “Molecular Mimicry as a Mechanism of Viral Immune Evasion and Autoimmunity” (https://doi.org/10.1101/202....

Thank you for sharing this very interesting article.

In this article, you stated that “Molecular mimicry explains portions of the multiple sclerosis auto-antibodyome”. I suggest that you should take the findings of Martins, et al. (2023) [1] into consideration. These authors pointed out that although the amount of antigenic sharing between hosts and both pathogenic and non-pathogenic parasites and bacteria is massive, molecular mimicry by itself is not a sufficient factor to disrupt intact self-tolerance mechanisms.

In this context, in my recent paper (https://www.aimspress.com/a... [2], I provided the missing link between molecular mimicry and the activation of autoimmune responses. I figured out that activation of the purinergic P2X7R expressed on regulatory T cells is potentially required for molecular mimicry to activate autoimmune responses. This occurs only after repeated or high-dose microbe infection and not after a single low-dose infection. I also presented a novel model of immune responses in mammals to foreign antigens, self-antigens and antigens with molecular mimicry.

I hope that these comments will help to improve the quality of your paper.

Kind regards,<br /> Djo Hasan

References

1. Martins YC, Jurberg AD, Daniel-Ribeiro CT. Visiting Molecular Mimicry Once More: Pathogenicity, Virulence, and Autoimmunity. Microorganisms. 2023;11(6). doi: 10.3390/microorganisms11061472.
2. Hasan D. Purinergic P2X7R expressed on regulatory T cells potentially links molecular mimicry to autoimmune responses. AIMS Allergy and Immunology. 2024;8(2):80-123. doi: 10.3934/Allergy.2024006.

On 2024-05-30 10:18:07, user Prof. T. K. Wood wrote:

Again, growth on methane was achieved by reversing methanogenesis in 2016 by obtaining active Mcr for the first time in a pure culture (ref 20), so the Introduction remains misleading (line 48, 'in their natural state') and line 323 is misleading:

"...ANME-1 MCR may allow Methanosarcina to perform AOM (20 )." We used multiple lines of evidence to demonstrate growth in 2016 and subsequent papers converted methane to lactate and even created a microbial fuel cell using methane, hence both uncited works again confirm active Mcr so it is not appropriate to write "may allow" growth. Should the field refer to all of your results as "may be' valid?

Moreover, in effect, the recombinant strain we produced was the way Nature created ANME, according to your work here (by altering Mcr) so our work is relevant to your report here as validation of this study, and the Introduction in this second version is still misleading.

On 2024-05-26 21:40:46, user Prof. T. K. Wood wrote:

Line 47: this statement is false: "Though there is not yet strong evidence that backwards carbon flow can be coupled to growth in either methanogens or ANME,.." as ref 20 was seminal in reversing methanogenesis in a methanogen by cloning Mcr and showing growth for the first time on methane in the engineered methanogen.

On 2024-05-30 09:18:41, user Sam Calis wrote:

Is there list of the peptides identified with the subtiligase biotin-assay availlable somewhere?

On 2024-05-30 05:42:05, user cong wrote:

We are trying to install Nanomotif in our server. We tried all of the install methods, and the major install looks good. However, when we tried nanomotif MTase-linker install, the following error was shown. It seems that module 'snakemake' had some issues. We then checked the 'snakemake' install and found we had snakemake==8.12.0. Is there any method to solve the problem for MTase-linker install?

Thank you very much!

$ nanomotif MTase-linker install<br /> /home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/mtase_linker/setup.smk<br /> Traceback (most recent call last):<br /> File "/home/miniconda3/envs/nanomotif/bin/nanomotif", line 10, in <module><br /> sys.exit(main())<br /> ^^^^^^<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/main.py", line 513, in main<br /> mtase_linker(args)<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/main.py", line 475, in mtase_linker<br /> snakemake_create_environments(args)<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/mtase_linker/dependencies.py", line 24, in snakemake_create_environments<br /> status = snakemake.snakemake(snakefile,<br /> ^^^^^^^^^^^^^^^^^^^<br /> AttributeError: module 'snakemake' has no attribute 'snakemake'

On 2024-05-02 21:22:33, user Alex Crits-Christoph wrote:

Nanomotif looks like a fine tool, especially for metagenomics, and I have no doubt it will push the prokaryotic methylation nanopore field further!

I was curious if you would be able to benchmark it more, as the benchmark presented in Fig 1C is quite limited in scope, and could be expanded. We have shared R10.4.1 data for a variety of microbes that you might be interested in benchmarking on, include some with paired PacBio data in REBASE (which is not ground truth, but a good comparision):

aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/mapped_bams/ . aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/reference_genomes/ . aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/pod5/ .

It is also worth noting that the comparison to MicrobeMod is a bit limited due to the reason that you note here: "The low motif recall of MicrobeMod, at high coverage and high motif occurrence settings, primarily stems from identification of similar motifs that are not identical to the benchmarking motif, e.g. SNGAm6TC instead of GAm6T".

Despite this, overall my sense is very that nanomotif's motif calling will be likely superior in many circumstances to STREME in the context of prokaryotic methylation. Probably the best way to evaluate methylation motifs would be with some manual inspection after running multiple tools (and parameters).

On 2024-05-30 05:18:38, user Severin Lechner wrote:

Thanks for looking into the downstream effects of HDAC inhibitors in various cells and on several levels.

It would be interesting to compare the results to recently published data on proteomics and phosphoproteomics response to a broad panel of HDAC inhibitors, such as: <br /> - Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics, Cell Rep. 2024<br /> - Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics, Nat Biotech. 2024

Further, the Abexinostat selectivity data in the main text lacks a reference. The statement also does not fully agree with the recently updated target selectivity landscape of HDAC inhibitors, where Abexinostat is shown to bind HDAC10 and the off-target MBLAC2 with substantially higher affinity than HDAC1: <br /> - Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target, 2022. Nat Chem Bio

On 2024-05-29 22:16:44, user kdkorthauer wrote:

Just curious whether your analyses adjusted for variability in blood cell type composition? Thanks!

On 2024-05-29 13:09:14, user Alex Crits-Christoph wrote:

KrakenUniq reports the coverage ("breadth of coverage", the percentage of the reference genome that is covered by sequencing reads) of each reference genome that may be present in the sample. Breadth information is a key way of determining true positive from false positive hits in metagenomics: almost all false positives are characterized by low breadth of coverage, as in these cases, reads only mapped to a fraction of the reference.

The authors should report coverage from KrakenUniq for their analyses; only counts are provided in the supplementary, but coverage values would be more informative for determining whether a microbial genome is present in a sample. For a brief discussion of this see:

https://instrain.readthedoc...

Further, the authors could then consider employing a minimum breadth cutoff to further separate true from false positives.

Finally the authors could also consider comparing to approaches that incorporate breadth automatically, such as:

https://github.com/bluenote...<br /> https://sourmash.readthedoc...<br /> https://instrain.readthedoc...

On 2024-05-29 08:20:26, user Alexey Belogurov Jr. wrote:

Manuscript has been published Chernov AS, Rodionov MV, Kazakov VA, Ivanova KA, Meshcheryakov FA, Kudriaeva AA, Gabibov AG, Telegin GB, Belogurov AA Jr. CCR5/CXCR3 antagonist TAK-779 prevents diffuse alveolar damage of the lung in the murine model of the acute respiratory distress syndrome. Front Pharmacol. 2024 Feb 21;15:1351655. doi: 10.3389/fphar.2024.1351655. PMID: 38449806; PMCID: PMC10915062.

On 2024-05-29 07:40:40, user PengLong li wrote:

Dear professor Bahlburg,

Hello. I'm very sorry to bother you in your busy schedule.

My name is Penglong Li, and I am a master's student at Dalian Ocean University in China. I have been focusing on the analysis of Antarctic krill resources using echogram images, a topic that greatly interests me. I recently came across your paper titled "An open and lightweight method to analyze the vertical distribution of pelagic organisms using echogram screenshots," which has been immensely inspiring for my research.

I am currently attempting to replicate the methodology presented in your paper. However, I have encountered some difficulties, particularly with accessing the source code. The link provided in your paper (https://sandbox.zenodo.org/... appears to be inactive.

I would be extremely grateful if you could share the echogram color matching program and other source code mentioned in the paper. Having access to these resources would greatly assist me in my research and help me better understand and apply your methods.

Regardless of your decision, I wish you the very best. Thank you for your time and consideration. Your help would be immensely appreciated, and I am deeply grateful for any assistance you can provide.

Wishing you good health and continued success in your work.

Best regards,<br /> li.pen.long0506@gmail.com<br /> Penglong Li<br /> Dalian Ocean University

On 2024-05-29 06:50:45, user theNiessingLabs wrote:

The authors show in Figures 3 - 4 and in Table 2 in silico-docking studies with an alphafold2-model of PURA as template. In this model, PUR-repeat III is shown as a monomer with an awkward-looking fold. The authors use this docking to suggest a direct interaction between PURA and GLUT1. <br /> Unfortunately, the authors seem to ignore that PUR repeats do not exist as single, monomeric repeats but require dimerization. For repeat III of Drosophila PURA, a high-resolution structure of its homodimeric domain has been reported already in 2016:<br /> https://elifesciences.org/a...<br /> PDB-ID: 5FGO<br /> For repeat III of the human PURA (as used in this study), more recently the homodimeric high-resolution domain structure has also been published:<br /> https://elifesciences.org/a...<br /> PDB-ID: 8CHW<br /> Considering these experimental structures, Figures 3-4 and Table 2 refer to unphysiological folds. As a result, conclusions drawn from these figures have to be considered as entirely wrong.

On 2024-05-28 03:20:10, user Samuel W. James wrote:

As a specialist in earthworm phylogenetics and taxonomy, I would really like to see an expanded taxon set within earthworms, including several cases where aquatic to terrestrial (and back) habitat shifts have taken place. There are even some where earthworms have colonized marine shore habitats. My colleague Christer Erseus who works on non-earthworm clitellates could also make some intelligent suggestions for future work on lineages that have transitioned from fresh to marine water environments or vice versa, as well as aquatic / terrestrial shifts. <br /> Nevertheless, terrestrial soils are basically aquatic environments, in that earthworms and Enchytraeidae ( and the soil-dwelling polychaete Hrabiella (I think) depend on free water and water films on soil particles and their body surfaces.

On 2024-05-26 19:15:45, user Peter-Bram 't Hoen wrote:

Dear authors, <br /> Interesting paper. It is good to see that this paper largely confirms our findings published in 2006: https://doi.org/10.1016/j.n...<br /> Peter-Bram 't Hoen

On 2024-05-25 17:46:48, user David Hornby wrote:

This manuscript is now published at Hurd, P.J. Al-Swailem, A.M. Bin Dukhyil, A.A.A. Sheikh, Q.I. Al-Ghanim, A.A. Alfageih, L. Matin, M. Yueh Ting, L.u. Abdalgelel, A. Florence, J. Mohammed Al- Shemirti, Al Harbi, S. Brown, P.E. Hornby, D.P. Systemic Mutational Rescue in Escherichia Coli Elicited by a Valency Dependent, High Affinity Protein DNA Interaction. Journal of Bioinformatics and Systems Biology. 6 (2023): 97-109.

On 2024-05-25 13:05:40, user Leando Cruz wrote:

Very intersting work. I would like to know the opinion of the author on the paper "Modulation of alpha-synuclein phase separation by biomolecules", since the authors were the first to propose the formation of biomolecular condensates of the protein mediated by spermine

On 2024-05-24 11:13:50, user Marcelo R. S. Briones wrote:

This paper was peer reviewed and published (May 27, 2024) in the journal "Viruses" https://www.mdpi.com/1999-4...

On 2024-05-23 02:12:03, user Adrian wrote:

This article was already peer-reviewed and published in NAR https://academic.oup.com/na...

On 2024-05-22 17:31:24, user lf wrote:

A version of this paper is now published in the journal Epigenetic at the following link https://www.tandfonline.com...

On 2024-05-22 15:01:07, user Donald R. Forsdyke wrote:

The authors cite a paper in PLOS Biology that was first posted as a preprint paper here in bioRxiv see Johri et al. 2021. The four comments I added to the Johri preprint paper have now been updated, noting the intriguing new k-mer analysis of Roberts and Josephs (2024).

On 2024-05-22 14:43:59, user Donald R. Forsdyke wrote:

The SSRN preprint mentioned previously (see four comments) has now (2024) been formally published under the same ("three historians") title in Theory in Biosciences (143(1): 1-26). One of the three (William J. Provine) having died in 2015, I now sadly report the passing of Mark Boyer Adams (May 9th, 2024). The paper included the work of the remaining historian (myself). This built on the DNA studies of Erwin Chargaff and my k-mer and nucleic structure analyses.

The formally published final version of Johri et al. is available in PLOS Biology. Their admonition to "carefully define ... underlying uncertainties" has resurfaced regarding "Lewontin's paradox." Citing this, Roberts and Josephs have posted a new bioRxiv preprint (May 19th 2024) entitled: "Previously unmeasured genetic diversity explains part of Lewontin’s paradox in a k-mer-based meta-analysis of 112 plant species" (see: Roberts and Josephs 2024).

On 2024-05-22 09:19:21, user Mouille wrote:

If you download it.... and read it...please comment it ! We really need your feedback ! Thank you. Grégory et al. !

On 2024-05-22 06:46:04, user Marc RobinsonRechavi wrote:

In the methods, you write<br /> "HDF5 files with the approximations for each organism are available on FigShare at https://figshare.com/accoun...."

But the figshare link doesn't work :(

On 2024-05-21 11:13:03, user dirkfaltin wrote:

Interesting paper. However, I'm afraid you are mixing up a lot of concepts that should be kept separate. Ethnonyms like Goths, Langobards and Frisians are political terms. They are not biological categories. Terms like "Wielbark Goths" (847) are entirely nonsensical. We simply don't know how the people of the Wielbark culture identified or were identified by others. Most likely they had never heard the name Goths and were not called so by outsiders. The name Goths appears only later in an entirely different region. We also don't know of the earlier "Gutans" had anything to do with the Wielbark-people or the later historical Goths. If you investigate the remains of a person buried in a cemetary that may have belonged to the Langobards, you still don't know how the individual identified whose DNA you extracted. Historians have been very careful to workout the problems that arise from mixing up ethnic/political terminology with archaeology. This paper revives the old mistakes by mixing up ethnic/political terms with archaeological material culture and biology.

On 2024-03-22 13:47:09, user Georg wrote:

As for the identification of the so-called East Scandinavian cluster associated with the I1 Y haplogroup, conclusions about the Baltic source are premature - the isolated autosomal complex is characteristic of groups of hunter-gatherers found from the Mesolithic Iron Gates, Neolithic Northern Germany ostorf003 and possibly EastBaltic

On 2024-03-18 17:57:45, user Martin Rundkvist wrote:

I have a few suggestions for making this fascinating paper more comprehensible to archaeologists and historians. I have edited about 120 issues of two journals. Feel free to get in touch.

On 2024-05-20 00:22:12, user Alexis Rohou wrote:

I was asked to review a version of the manuscript for a journal. Below are my comments to the authors.

In this manuscript Shub and colleagues present MIC, a new tool for the analysis of three-dimensional macromolecular structures obtained using x-ray diffraction or cryogenic electron microscopy (cryoEM). Given an atomic model featuring water molecules and/or ions, and the corresponding 3D map data, MIC automatically labels each putative water and/or ion location with a guess water or ion identity.

Thanks to improvements in cryoEM instrumentation and data analysis, 3D maps can now frequently be obtained at resolutions better than 2.7Å, so that the problem of correctly labeling small map features as either water or ion presents itself more frequently to practictioners, myself included. In that context the availability of a well-characterized, open-source and highly performant classifier of ion/water density features is timely and most welcome.

I found the manuscript to be well written, the description of the method and how it was tested clear (though as a non-expert I had trouble following the description of the network architecture and of the feature attribution methodology), and the results convincing. Most questions that arose as I was reading early parts of the manuscript (e.g. regarding the influence of slight errors in modeling of protein atoms on the labeling of ions/water) were answered in later parts. I only have minor feedback & suggestions for the authors and otherwise am supportive of publication.

Here's the feedback I have:<br /> - lines 47-48: Perhaps... but I wonder if the authors are aware of Ravera et al (Nature, 2022)... and there may be a few other reports I'm unaware of myself... I wonder whether a radial average profile could be used as part of the fingerprint in future versions of MIC to improve the quality of the labeling by the network. Perhaps the authors could comment (in the discussion?) about whether incorporating experimental map features could be possible in future work?<br /> - lines 189-190: "this simple metric showed statistically significant separation between<br /> test set predictions that agreed and disagreed with the deposited PBD label": I found this sentence confusing at first, and still do to an extent. To me the syntax did not convey the meaning of the figure unambiguously. To spell it out: <br /> -- My understanding on first reading was: when a site lands near a boundary (i.e. the model is uncertain), the model's prediction tends to disagree with the deposited PDB label. Doesn't that mean that if the user gets a result that is assigned low confidence, they should actually assume that the identity assigned by MIC is wrong? That would seem to be problematic... wouldn't it?<br /> -- Having reviewed Fig 2e, I now think the text was a bit confusing. My interpretation of the figure: at low confidence scores, say around 0.5, it's only slighlty more likely than not that there's a disagreement with the PDB; this is more in line with the behavior I would have hoped for... i.e. when there's uncertainty, it's a close call - not necessarily was the wrong label given...<br /> - line 213: "The revised overall test set accuracy following manual annotation is 83.3%". This is impressive. How will this translate to lower-resolution structures, where there will be more error in atom positions... I am thinking back to the fact that they authors found that fingerprinting worked best when using very fine shells... Perhaps this should be discussed somewhere [I think this is touched on in the paragraph ending line 269]<br /> - line 269: Right. That touches on my earlier question. When you get to the ~3Å range, unless you happen to have your coordinating side chains modeled really well, MIC is going to be quite prone to error. [I still think this topic of accuracy as a function of resolution should be returned to in the discussion]<br /> - line 432: "MIC achieves incredible accuracy". Hah! I think the authors should aim for the readers to actually believe the claimed accuracy, and it is unhelpful to characterize the accuracy as "incredible". I found the manuscript credible overall ;)<br /> - line 495-498: "limiting all calculated (...) to be identity agnostic" - I don't understand this. Might be worth spelling things out for non-specialists like myself<br /> - line 512: "non/prune-fifp" I think should be "non-prune/fifp"<br /> - line 513: "prune-eifp" I think should be "prune/eifp"

Alexis Rohou<br /> May 2024

On 2024-05-18 21:03:14, user Camilla Forsberg wrote:

This preprint has now been revised and published in CELL:<br /> https://www.cell.com/cell/f...

On 2024-05-18 16:14:04, user Carol Greider wrote:

This paper is now published in Science<br /> DOI: 10.1126/science.ado0431

On 2024-05-17 06:43:31, user Bayden D. Russell wrote:

Now published in Science Advances doi: 10.1126/sciadv.abp8747

On 2024-05-17 05:05:22, user Julian C wrote:

Updated citation: PLoS ONE 19(5): e0302696. https://doi.org/10.1371/jou...

On 2024-05-07 17:33:00, user Julian C wrote:

(From the corresponding author) To appear in PLOS ONE.

On 2024-05-16 13:44:41, user Jiri Hulcr wrote:

Nice article, we are hoping that we could use the genome sequence for our current work. <br /> I would recommend that the authors do not replicate the standard adage of how we have to study this beetle because it is a pest that is difficult to control. There are piles of literature on this topic, including two comprehensive compendia by the Forest Service, dedicated to the biology and management of this species. Foresters know very well how to manage forests to avoid outbreaks of the Southern pine beetle. There is a multi-state program for monitoring the population of the species, and predicting its local outbreaks. Case in point - the beetle was pretty much eradicated from the state of Texas, which is presumably why the authors had to use material from Mississippi. <br /> Studying genetics in order to "kill the pest" seems mistargeted. We know nearly all bark beetle outbreaks are a symptom of excessive stand density, warming climate, or introductions of invasive species, i.e., human-caused. So justifying this great research by the need to control the insects is not very convincing. The many incredible biological features of this insect would make a much more interesting justification.

On 2024-05-15 13:15:57, user Ruben Perez wrote:

This preprint has been published in Virus Evolution (10.1093/ve/veae031). The title has been slightly modified: “Highly pathogenic avian influenza H5N1 virus infections in pinnipeds and seabirds in Uruguay: implications for bird-mammal transmission in South America”.

On 2024-05-14 04:54:31, user Erick Nedd wrote:

1. Limited Sample Size: In Figure 1A-E, I noticed that only 18 out of the 54 subjects were included in the comparison of biological age versus chronological age in EL subjects. Due to the already limited nature of having a large sample size of centenarians for research studies, it may be more effective to include more participants because having a smaller sample size may limit the generalizability of findings and necessitate caution in applying results to broader populations or conclusions about aging.

2. Including more controls: In Figure 3 where the forward programming of EL-specific iPSCs into cortical neurons was analyzed, I noticed that there was no control of an established cortical neuron cell line. Including this control, would be helpful in comparing the differences and similarities between the differentiated iPSCs and the cortical neurons, and would further help to confirm the pluripotency of the iPSCs created in this experiment.

3. Inclusion of Demographic Information: Including the demographic information of centenarians or the countries from which they come from would be helpful in contextualizing the study and understanding the possible influences on biological age. Considering that factors such as, geographical location, lifestyle habits and access to healthcare can all influence the aging process, including the demographic information of participants may illustrate environmental or genetic factors that may lead to exceptional longevity. Furthermore, having iPSCs from diverse populations may lead to more robust findings in that they would better represent the genetic variation across the globe.

4. Supplemental figures: While the inclusion of the supplemental figures was interesting in seeing the different population of immune cells in centenarians, there was not sufficient information for readers to see how these immune cell populations compared to offspring or their spouses. Having a control of an offsprings’ spouse would help your audience understand how the population of immune cells in centenarians may have lead them to living a longer life, and provide a clearer picture of the role of immune cells in longevity.

On 2024-05-13 11:39:30, user Alice Risely wrote:

This is a great experiment for looking at changes in serum metabolites over migratory stages. This is valuable information. However, it is a shame that the gut microbiome part of the study is only rudimentary - the study would be much stronger if it could link changes to metabolites with gut microbiota variation, which requires measuring the gut microbiota across all stages and using metagenomics or universal primers. As such, I think the 'gut microbiome adaptions' in the title is misleading.

On 2024-05-12 22:14:39, user Elliot Swartz wrote:

Please see a summary of problems with this study: https://gfi.org/wp-content/...

On 2024-05-11 17:42:26, user Thierry Grange wrote:

A revised version of this manuscript has been published in October 2023:

https://doi.org/10.1038/s41...<br /> Genome sequences of 36,000- to 37,000-year-old modern humans at Buran-Kaya III in Crimea<br /> E. Andrew Bennett, Oğuzhan Parasayan, Sandrine Prat, Stéphane Péan, Laurent Crépin, Alexandr Yanevich, Thierry Grange & Eva-Maria Geigl <br /> Nature Ecology & Evolution<br /> volume 7, pages 2160–2172 (2023)

On 2024-05-10 21:05:06, user disqus_gM8nbME1Vj wrote:

In discussion, para 2, 19th line, the author mentioned that the expression of "the induction of Fe uptake related genes such as FRO2, IRT1 was also not compromised in the pye mutant unlike hy5 mutant" and refered to the "Figure S2".<br /> But, in "Figure S2", it appears that FRO2 and IRT1 levels are lower in pye than in hy5 mutant as compared to the WT.

On 2024-05-10 08:11:47, user Stefano Vianello wrote:

Dear Dr. Blotenburg,

I'm Stefano, the author of REF 20 re endoderm-rich gastruloids. In the Discussion section of your manuscript you write that

[REF20] maintained mESCs in 2i-medium and reported faithful emergence of endoderm cells

. Given the importance of mESC culture conditions in your analyses and possible future interpretations (at least, re endoderm), I wanted to point out that — following the practice of the lab I was working in at the time — mESCs were not grown in the classic 2i medium (2i in N2B27), but in fact in a 2i in ES+LIF medium (exact recipe in REF20's Materials & Methods > Cell culture). Based on gastruloid end-phenotype alone (of those shown in FigS1), I would guess this atypical mESCs culture medium is most closely matched by your culture condition 3 (and possibly condition 4), and that those conditions (though they were not selected for scRNAseq) are giving rise to endoderm-rich gastruloids.

Sincerely,<br /> Stefano Vianello

On 2024-05-09 19:30:15, user Sophie Astrof wrote:

This paper has been published:

https://doi.org/10.1161/CIR... Circulation Research. 2024;134:e112–e132

On 2024-05-09 13:09:41, user Peter Ellis wrote:

Fascinating work but it doesn’t complicate the central dogma in any way regardless of what inaccurate textbooks say.

https://www.researchgate.ne...

Crick was quite specific: the central dogma simply says that translation is irreversible.

On 2024-05-09 08:52:36, user Mihail Halachev wrote:

Apologies for the late reply. The variant in the LOXHD1 gene<br /> (as all other enriched variants listed in Tables 1-5) can be found in ClinVar by searching for AlleleID = 176561, rather than by variation ID.

https://www.ncbi.nlm.nih.go...

On 2024-05-08 16:54:50, user Jorge Soberon wrote:

Many people have used ellipsoids (Mahalanobis distance) for niche modelling in the past. I think you need to consult, at least:

Farber, O. and Kadmon, R., 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological modelling, 160(1-2), pp.115-130.

Jiménez, L., Soberón, J., Christen, J.A. and Soto, D., 2019. On the problem of modeling a fundamental niche from occurrence data. Ecological Modelling, 397, pp.74-83.

Drake, J.M., 2015. Range bagging: a new method for ecological niche modelling from presence-only data. Journal of the Royal Society Interface, 12(107), p.20150086.

Hirzel, A.H., Hausser, J., Chessel, D. and Perrin, N., 2002. Ecological‐niche factor analysis: how to compute habitat‐suitability maps without absence data?. Ecology, 83(7), pp.2027-2036.

On 2024-05-08 12:59:32, user Yuri Pavlov wrote:

"The artifacts of the dataset were removed by the authors while publishing the dataset" statement is false. The data you downloaded are raw EEG data (as stated in the dataset description. All analyses are therefore performed on data containing a lot of eye movement and muscle artifacts making your classification algorithm useless.

On 2024-05-07 17:57:42, user Dimitrius Santiago P. S. F. Gu wrote:

Hi, the manuscript has just been published with open access in the Journal of Sarcopenia, Cachexia and Muscle! Please access https://onlinelibrary.wiley... for the latest version.

On 2024-05-07 17:37:12, user Julian C wrote:

(From the author) To appear as a book chapter in: Ruiz Romero C, Calamia V and Lourido L (Eds), Protein Arrays: Methods and Applications, Electronic ISSN 1940-6029, Print ISSN 1064-3745, Springer Nature.

On 2024-05-06 11:02:33, user Roman wrote:

There is already another CRISPR method out there, called CREATE (https://pubmed.ncbi.nlm.nih... so maybe the authors want to pick a different acronym so that readers don't get confused.

On 2024-05-06 08:20:36, user Liheng Luo wrote:

Your work, CRISPR-GPT is groundbreaking, bridging the gap between complex gene-editing technology and researchers from various fields. The potential for accelerating biological discovery is immense, and I’m excited to see its impact on future research. <br /> I am eager to see this technology in action and would greatly appreciate the opportunity to explore a demo of the author’s website. Such hands-on experience would provide invaluable insight into the practical applications of CRISPR-GPT.

On 2024-05-06 08:02:44, user Daniel Guzman Llorens wrote:

Congratulations to the team, it is a great article. I found the results very insightful.<br /> While reading, I seem to have found an error in the insulin intensity histogram in Figure 3, as both A and C seem to be the same histogram.

On 2024-05-04 20:50:43, user Cameron Cohen wrote:

Fascinating read! Can’t wait to see where this project goes!

On 2024-05-04 01:52:43, user priyanka.bajaj3193@gmail.com wrote:

In this study the authors comprehensively examined the mutational effects on PLpro proteolytic activity and stability. The authors have designed a FRET-based assay composed of N-terminal mClover3 donor and C-terminal m-Ruby3 acceptor fluorophores separated by a linker containing the Nsp2/3 PLpro cleavage motif to measure the proteolytic activity of PLpro. From DMS data the authors infer PLpro active site mutations ablates activity. Their study also revealed residues required for cleavage of the Nsp2/3 site, identified features of substrate binding pocket and the sequence requirements of the blocking loop. The authors have given explanations for their observations in the Discussion section. Overall, the paper is supported with follow-up enzymology and crystallography experiments of key residues. The major limitation of this study is leaky expression of mutations can mask clinically relevant mutations that can arise due to viral evolution and might have the potential to evade inhibitor treatment. Study of such mutations can provide more information about the potential escape routes open to the virus to evade developing therapeutics. Moreover, incorporation of statistical analysis could strengthen the confidence in inferences drawn from the deep sequencing data and improve the quality of the manuscript. <br /> The following points can improve the quality of the manuscript:<br /> Major points<br /> 1. In Figure 1f, it is unclear that why is the PLpro activity increasing with increase in inhibitor concentration? Perhaps the Y axis is mislabeled as while inhibitor concentration is increasing, PLpro activity should decrease. However, FRET signal would increase (and maybe should be the axis label), since there will be no cleavage. <br /> 2. Line 175 – 178 - What does 0 represent in the normalized dataset? What is the rationale used for selecting minimum 10 reads in the unselected library as the read cut-off. 10 reads is pretty low cut-off. From the data, it seems the distribution tails off before cut-off chosen for the s.d.- by eye. 0.3 s.d and 20 as read cut-off might be a better option to eliminate sequencing artifacts.<br /> 3. Line 187-190 – What is the number of reads for the mutants that showed lower activity scores? There is a possibility that due to low read cutoff, these mutants might be lying in the range with low reads in the unselected library.<br /> 4. Line 223-224 – Authors mention they find a good correlation between activity and abundance score. Although this is noticeable from the scatter plot but supporting high-throughput data with statistical parameters like pearson correlation coefficient, a metric that provides comparison between 2 datasets will make this data reporting more quantitative and informative.<br /> 5. Line 1340- Figure 3b- What do authors mean by variants with small enough error? Please be precise.<br /> 6. Line 315-319/ Line 1550-1556- Extended data Fig 15c – It is difficult to interpret the inference reported that is based on the data in Extended Fig 15. There is no data reported for Normalized AMC cleavage for Y268W. Interpretation can be more comprehensible by plotting a scatter plot between the Normalized Activity Fitness Scores obtained from DMS data and Normalized AMC cleavage (%). Through this plot, the reader can easily make out the outlier.<br /> 7. Line 364-367 – Authors mention “M208W strikingly increases the protein melting temperature by over 5C, indicating a substantial improvement in thermal stability. Increased stability, and thus reduced turnover in cells, may provide a mechanism to explain leaky expression in our cellular assay and increased yield of recombinant protein for E.coli expression.” Since, leaky expression is a different issue, it is confusing why will leaky expression be a plausible reason for increased stability but less activity? <br /> 8. Since Extended data Fig 11a shows that variants display substantial amount of leaky expression, how have the authors taken this information into account while inferring results from DMS activity scores, especially since they are quantifying at the RNA level and not at the DNA level? Can the activity scores obtained for the mutants be normalized to leaky expression scores in some way, for example by subtracting the scores obtained from the leaky expression dataset in order to measure the true activity of each mutant?<br /> 9. Solvents are known to affect an enzyme’s activity, selectivity and stability. In Figure 5, authors should consider and comment about the role of solvent in understanding the mechanism of Michelis-Menten kinetics of M208 variants using substrates Z-RLRGG-AMC, Ubiquitin-Rhodamine and ISG15-Rhodamine.<br /> Minor points<br /> 1. Figure numbers need to be reformatted. Figure 3 onwards they are incorrectly labelled. For eg. ‘Fig 3’ is labelled as ‘Fig 1’.<br /> 2. Line 426-429 – In Figure 3b, L and R domains of papain should be labelled or highlighted in separate colors for the ease of understanding for the reader.<br /> 3. Overall, different DMS datasets obtained from different assays in the paper have different read cut-offs such as 10, 13 and 18. A consistent statistical logic for obtaining different read cut-offs across different DMS datasets will be helpful. Also, increasing the read cut-off might improve the data quality and minimize sequencing artefacts.

• Reviewed by Priyanka Bajaj and James Fraser (UCSF)

On 2024-05-03 21:48:51, user Michael L. wrote:

This manuscript has now been combined with https://www.biorxiv.org/con... and published as https://pubmed.ncbi.nlm.nih...

On 2024-05-03 21:47:59, user Michael L. wrote:

This manuscript has now been combined with https://www.biorxiv.org/con... and published as https://pubmed.ncbi.nlm.nih...

On 2024-05-03 10:36:51, user Samvid Kurlekar wrote:

Really great and useful work and an astounding tour de force - enjoyed reading it!<br /> I was wondering if the authors might please comment on whether the iPT cells expressed significantly higher levels of lncRNAs such as Neat1 or Malat1 when compared to 'normal' PT cells. We have seen these Neat1/Malat1-rich PT cells in our own scRNA-seq dataset (from mice) but were unable to detect HAVCR1 or VCAMI. However, genes associated with VCAM1-positivity were detected and these PT cells (which we called PT Class A) were present in Control kidneys and were seen in situ by RNAScope too. <br /> I would be quite interested to know if the iPT-VCAM1+ population you have comprehensively described matches the PT Class A cells we saw.

On 2024-05-03 08:40:27, user Gregory Ehx wrote:

This article is now published in Leukemia doi 10.1038/s41375-024-02250-6

On 2024-05-01 23:26:21, user Guei-Sheung Liu wrote:

The article has published in Hum Gene Ther.<br /> Utility of Self-Destructing CRISPR/Cas Constructs for Targeted Gene Editing in the Retina.<br /> Li F, Hung SSC, Mohd Khalid MKN, Wang JH, Chrysostomou V, Wong VHY, Singh V, Wing K, Tu L, Bender JA, Pébay A, King AE, Cook AL, Wong RCB, Bui BV, Hewitt AW, Liu GS.<br /> Hum Gene Ther. 2019 Nov;30(11):1349-1360. doi: 10.1089/hum.2019.021. Epub 2019 Oct 25. PMID: 31373227

On 2024-05-01 23:23:51, user Guei-Sheung Liu wrote:

The article has now publshed in Nucleic Acid Ther.<br /> An Integrative Multi-Omics Analysis Reveals MicroRNA-143 as a Potential Therapeutic to Attenuate Retinal Angiogenesis.<br /> Wang JH, Chuang YF, Chen J, Singh V, Lin FL, Wilson R, Tu L, Ma C, Wong RCB, Wang PY, Zhong J, Hewitt AW, van Wijngaarden P, Dusting GJ, Liu GS.<br /> Nucleic Acid Ther. 2022 Aug;32(4):251-266. doi: 10.1089/nat.2021.0111. Epub 2022 Mar 31. PMID: 35363088

On 2024-05-01 23:21:11, user Guei-Sheung Liu wrote:

The article has now published in Pharmacol Res. 2023 Jan:187:106617. doi: 10.1016/j.phrs.2022.106617. Epub 2022 Dec 16.

On 2024-05-01 17:47:25, user Kevan Shokat wrote:

Fantastic study of the clamping effects of Rocaglamide analogs across the helicase family. I particularly like the different effects of nucleotide and that ATP rather than AMP-PNP can stabilize different helicases as shown in Figure 5F. I wonder if mixtures of ATP and smaller concentrations of AMP-PNP could let helicases work (ATP) and then trap (AMP-PNP). Great study of so many dimensions of this assay! Congratulations!

On 2024-05-01 16:53:49, user Timothy Tomkins wrote:

This manuscript explores the mechanisms involved with decreased immunity due to aging. It does this by looking at the microbiome found within mice at different ages, then looking at increased inflammation utilizing immunoassays. The results show increased innate immunity and inflammatory signals, showing that an older mice’s microbiome acts differently on the TLR signaling system. The manuscript excels on the immunological side; however, the microbiome side of the study lacks quality control and explanation. I would recommend contacting a microbiome expert to get more insight into the field, as there is much missing from this report.

This review focuses on the microbiome side of the report, which has quite a few problems to work on.

1. Polymerase Chain Reaction (PCR) details are missing, such as the primers used, the polymerase used, and the procedure used, such as the temperature, timing, number of cycles. The PCR is not repeatable based on what is said in the methods. Refer to the paper ‘The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data’ at DIO: https:// doi.org/10.1128/mSphere.001....

2. When sequencing the PCR data, the number of reads per sample is not stated. This is important to know if the coverage of the communities is measured. The authors also do not include the observed number of taxa to compare to the Chao1 numbers to determine coverage. This is shown in Figure 1B, where the chao1 number is very low. The taxa used for this graph is not stated, leaving the reader confused if it is the number of genera, or phyla. The lower number assumes phyla, while genera is the more commonly used in the field and should be in the hundreds. Showing the number of ASVs/OTUs in each sample is a common measure of alpha diversity.

3. The method to which the sequenced data was sorted is not given. The authors do not state if ASVs or OTU’s were utilized which leads to confusion in the way the dominant taxa were determined. It is also impossible to know if the sequenced data was corrected for 16S copy number, or for variance in genome size. Refer to ‘The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses’ at DIO: https://doi.org/10.1371/jou...

Next are some more minor issues that can be addressed.

1. At line 340, the instrument and settings for the zirconia beads are not explained.

2. No reference is given for the CLR method at line 385.

3. In the statistical analysis, it states that data was rarefied to the minimum library size, but that size is not mentioned.

4. Figure 1A states P=0.002, however no statistical test is given to get that P.

5. Figure 1C utilizes the words upregulated and downregulated which are used with expression data. I believe this is abundance of taxonomic groups, so different word choices would be more accurate, such as more or less represented. Further, you don’t know that age caused that, as implied by the phrase “by age.”

6. Timothy Tomkins, SHSU biomedical student.

On 2024-04-30 20:15:37, user Austin McIlhany wrote:

Fascinating paper both on the topics of cutting edge field of microbiomes and still-ongoing issues of human-caused environmental crises, specifically oil spillages. Here are a few ideas and questions I have that I think could possibly improve this paper:<br /> 1. Since this study focuses on N and P levels and hydrocarbon degradation, then the levels of N and P in the collected seawater need to be quantified.<br /> 2. I understand that it must not be under lab-ready circumstances to extract AND analyze the samples of the artic waters at the same exact location for a more accurate sampling of the microbiome<br /> 3. The levels in the three growth conditions of high, low, and ambient. It would be helpful to list all ingredients and their final concentrations for the three conditions as well.<br /> 4. Perhaps in the future, the use of transposons could also be used to track the genes that correlate with biodegradation of crude oil, and which strains/taxa of bacteria they originate. Making a isolated, pairwise and community could also help narrow it down.<br /> 5. PCR conditions for the V4 amplification should be described.<br /> 6. In the “differential abundance analysis,” were there adjustments made for different 16S copy numbers in different taxa? Different genome sizes? If not, this must be mentioned as a limitation.

大多数人认为分布式训练由于需要同步和通信，必然比单机训练慢，但作者认为Decoupled DiLoCo比传统同步方法快20倍以上，这挑战了人们对分布式训练速度的固有认知，展示了异步计算的潜力。

大多数人认为混合不同代际的硬件进行训练会降低性能或效率，但作者认为即使不同代际、不同速度的芯片混合使用，仍能达到与单一芯片类型训练相同的机器学习性能，这挑战了硬件必须同质化的行业共识。

大多数人认为硬件故障会显著降低分布式训练的效率和性能，但作者认为即使在硬件故障率极高的环境下，Decoupled DiLoCo仍能保持88%的有效训练率，而传统方法则暴跌至27%，这挑战了人们对故障容忍能力的传统认知。

大多数人认为分布式AI训练需要高度同步和紧密耦合的系统才能保证效率，但作者认为通过解耦的'计算岛屿'架构，即使局部硬件故障，系统其他部分仍能高效学习，因为故障被隔离了。这挑战了传统分布式训练必须保持同步的主流认知。

On 2024-04-30 14:51:04, user Luca Jovine wrote:

Published on 26 April 2024 as:

Elofsson A., Han L., Bianchi E., Wright G. J. & Jovine L.<br /> Deep learning insights into the architecture of the mammalian egg-sperm fusion synapse <br /> eLife 13:RP93131 (2024)<br /> DOI: https://doi.org/10.7554/eLi...<br /> PubMed: https://pubmed.ncbi.nlm.nih...

On 2024-04-30 09:45:18, user Federico Colombo wrote:

This article has been peer reviewed and published on https://onlinelibrary.wiley...

On 2024-04-29 21:54:07, user Yeshveer Singh wrote:

Final version of this article is published at MPMI. Here is the link: https://doi.org/10.1094/MPM...

On 2024-04-29 15:59:37, user Amber Gonzalez wrote:

Additional comments<br /> Hello! I am a Sam Houston State University graduate student enrolled in a microbiome course, BIOL5394. My overall impression of the manuscript is that it is excellent, and the information presented is helpful for forensic science. <br /> Abstract and Introduction<br /> The introduction is very clear and to the point. Does this study consider where the individual's death occurred, indoors or outdoors? I believe the environment in which a death occurred could alter the microbial community succession and potentially influence your given results. What ethical guidelines were followed when handling the cadavers?<br /> Methods<br /> Quality control <br /> • Need specific PCR protocol, like # cycles, temps, polymerase, etc. https://journals.asm.org/do... <br /> • There is no mention of the correction of the 16S copy number and variances in genome size for taxa identified ASVs. https://journals.plos.org/p... <br /> • Was coverage of communities measured and representatively sampled equally?<br /> o Include coverage measures such as Good’s coverage or Chao1.<br /> • Line 115 explicitly states the protocol for USA samples. What about the samples from Finland and Italy?<br /> • Lines 113-114 mention sections of the dissected internal organs but fail to mention the specific section. Is there a measured area region that was dissected? This could help with consistency in sampling.<br /> DNA Extraction and Sequencing <br /> • Lines 130-131 state the Greengenes database used to assign taxonomy was last updated in May 2013. More accurate identification results would come from a more recently updated 16S gene database.<br /> Statistical analyses<br /> • Good practice to include the complete list of packages and codes used for the analysis.<br /> Results<br /> Figure 3<br /> • PCoA assumes normal distribution of data. Need to show normality test or use NMDS.<br /> Figure 4<br /> • The taxa in this figure show excessive “unknowns.” I believe updating the database could improve this.<br /> Figure 5<br /> • Lines 256-259 mention the findings of ASV family are in class Bacteroidia, however after reviewing I believe the correct class is Betaproteobacteria.<br /> o Bacteroidia, Betaproteobacteria, and Clostidia are color-coded with very similar colors. Consider making the jump to the next class a bit more distinctive.<br /> Additional comments<br /> • The article mentions the sample size was 265, but when I add up the four category sample sizes, I get a sum of 262. Is this intentional, or a typo?<br /> • Of the 20 Finnish cadavers, why was the liver the only organ supplied?<br /> • Overall, it was a very interesting study!

On 2024-04-25 23:14:15, user Michelle Wille wrote:

This is an important study rapidly presenting key findings of sampling for HPAI in the Antarctic region. We have some concerns around the interpretation of the results - we beleive the diagnostic used is excellent at detecting a broad diversity of H5 viruses and is not specific to HPAI H5 and therefore it is unclear whether the authors detected HPAI H5N1 or LPAI H5Nx. A summary of our concerns has been presented here: https://www.preprints.org/m...

On 2024-04-25 14:29:24, user Heather Etchevers wrote:

New address for Dr. Patrice Quintana: Université Clermont Auvergne, INSERM, U1107, NEURO-DOL, Clermont-Ferrand, France.

On 2024-04-25 07:12:08, user Rafal Mostowy wrote:

Hi, it’s very interesting work! I was wondering if you could make your supplementary material available? I don’t see it posted with the preprint. Thank you

On 2024-04-24 16:37:09, user Q Li wrote:

Please link this preprint to the published paper https://rdcu.be/dFAvb <br /> Thank you!

On 2024-04-24 14:37:31, user Bonnie Thiel wrote:

This manuscript has been peer reviewed and published: https://journals.plos.org/p...

On 2024-04-24 07:40:24, user Julien Y. Dutheil wrote:

This article was published in PLoS Biology: https://doi.org/10.1371/jou...

On 2024-04-23 21:44:35, user Mattia FM Gerli wrote:

The peer reviewed version of this article has been published on Nature Medicine in March 2024 at the following DOI:

https://doi.org/10.1038/s41...

On 2024-04-23 17:09:36, user Moira C wrote:

Hello, the Yadav lab has shown data on a protein structurally similar to SLK in humans called TAOK1, I have linked their paper here https://www.science.org/doi.... Do you have any comment on the similarity between these two proteins especially since this other kinase seems to have an I-bar domain as well.

On 2024-04-23 12:36:47, user Alessandro Popoli wrote:

This paper highlights some valuable genetic elements and offers precise quantifications of subtle effects on fruit shape, which are attributed to a single STM autoregulatory element. While it is commendable that such effects have been observed and quantified with apparent robustness, this aspect alone does not compensate for the overall weaknesses of the study.

The significance of this autoactivation in fruit metamorphosis, as discussed, is exaggerated. Techniques such as imaging quantification and single-cell analysis are utilized, yet they seem to function more as ornamental enhancements rather than providing substantial contributions to the core narrative. For instance, the imaging quantification primarily shows proliferation within the stomatal lineages but fails to delve deeper into the overall growth mechanisms influencing fruit shape. Additionally, the single-cell analysis appears to be inadequately executed in several aspects.

A thorough reporting and discussion on the robustness of observations involving transgenic lines, such as GUS reporters, inducible lines, and mutants, is sorely missing. This is crucial, especially considering the minimal phenotypical effects of the cis-regulatory element under investigation. A detailed analysis is needed to ascertain whether the observed effects are indeed significant or simply artifacts stemming from genetic background noise.

Lastly, the selection analysis presented in Supplementary Figure 26 is conceptually flawed and should be reconsidered. The approach of segregating sequences with and without the binding site (BS) before analysis is fundamentally problematic. Selecting species that carry the BS and then asserting high conservation within this group compared to others is tautological and compromises the scientific integrity of the findings.

On 2024-04-23 10:07:33, user AngelPerezDiz wrote:

The final version of this manuscript has been published in Molecular Ecology journal:

Diz, A.P., & Skibinski, D.O.F. (2024). Patterns of admixture and introgression in a mosaic Mytilus galloprovincialis and Mytilus edulis hybrid zone in SW England. Molecular Ecology, 33, e17233. https://doi.org/10.1111/mec...

On 2024-04-23 07:19:50, user Rashidul Islam wrote:

This manuscript has been officially published in the British Journal of Cancer. We therefore kindly request to review the final published version of the manuscript.

Here is the paper:<br /> Islam, R., Heyer, J., Figura, M. et al. T cells in testicular germ cell tumors: new evidence of fundamental contributions by rare subsets. Br J Cancer (2024). https://doi.org/10.1038/s41...

On 2024-04-22 18:36:56, user HUANYUAN ZHANG-ZHENG wrote:

This manuscript has now been published: https://www.nature.com/arti...

On 2024-04-22 10:57:31, user Julien Y. Dutheil wrote:

This preprint was published in PLoS Computational Biology: https://doi.org/10.1371/jou...

On 2024-04-22 08:04:35, user Daniel Ryan wrote:

Now published in Nature Microbiology.<br /> https://www.nature.com/arti...

On 2024-04-19 10:09:16, user RG wrote:

Fascinating analysis & great dataset.

The question of PIE aside, which is a complex social and linguistic matter, there is in fact clear evidence of movement from the Balkan route into Anatolia in in fact clear and unequivocal, despite the claims expressed in this study.

The presence of I2a-L699 in Yassitepe (Lazaridis 2023), and its persistence into the Iron Age is clear testament to this. Contra to the analysis offered in L. et al (2023), that lineage is not a Balkan lineage - missing in all pre Bronze Age samples (Mathieson 2015, Penske 2023, Lazaridis 2023)- but from further north, esp the Dnipro valley, and more proximately Cernavoda C.

This establishes links between southeastern Europe and Anatolia, and exposes the limitations of inferences limited to one particular qpAdm set up.

It also supports the view supported by most linguists- that proto-anatolians arrived via the Balkans (without excluding more complex scenarios entailing convergence).

On 2024-04-18 19:38:40, user Rishav Mitra wrote:

Summary:<br /> Transglutaminase 2 (TG2) is a GTP binding/ protein-crosslinking enzyme with therapeutic potential in various conditions such as cancers, Celiac disease, and neurological disorders. TG2 is thought to have two major conformational states, an inactive GTP-bound closed state and a crosslinking-active Ca2+-bound open state. Other groups have previously reported X-ray structures of TG2 that reveal the structural basis for the regulation of transamidation activity by GTP/GDP and Ca2+. Although these studies have suggested that guanine nucleotides and Ca2+ allosterically regulate TG2 activity by inducing global conformational changes, direct evidence for conformational transitions has been lacking so far. The authors of this paper have previously shown that a small-molecule inhibitor, TTGM 5826, inhibits the protein crosslinking activity of TG2 by stabilizing the open conformation. Interestingly, TTGM 5826 prevented the growth of cancer cells which led the authors to conclude that the open conformation is cytotoxic. Therefore, locking TG2 in the open state by small molecules could lead to new therapeutic strategies. <br /> In this study, the authors have investigated how the binding of guanine nucleotides, calcium, and small-molecule inhibitors affects the open and closed conformational states of TG2 using small- angle X-ray scattering (SAXS) and single-particle cryoelectron microscopy (cryo-EM). Additionally, they focused on the discovery of improved small molecule inhibitors compared to TTGM 5826. The major success of this paper is the finding that TG2 can undergo a reversible conformational transition in solution between closed and open states under physiological GTP and Ca2+ concentrations. In addition, the authors have found a new conformational state inhibitor, LM11, that is more potent than TTGM 5826, although the evidence to support the connection between drug potency and TG2 conformational specificity in cells is weak. The authors show that the LM11-bound state has a different conformation from the Ca2+-bound open state. The major weakness of this paper is the lack of mechanistic information to explain the potency of LM11. Hopefully further structural studies will provide further details on the conformational changes induced by LM11 and other inhibitors. <br /> Major points:

In Figure 3D, the authors explained the different conformations between WT and the R580K mutant under GTP conditions by Kratky plots and fitting using CRYSOL. A Kratky plot normalized by Rg may be a better way to discuss the conformations since normalized Kratky plots emphasize conformational differences. In such a plot the weak shoulder in around q = 0.15Å-1 of “open dimer”, which likely comes from the dimer conformation's symmetry, can then be emphasized and discussed.<br /> In the text regarding Figure 4, the authors mentioned 3DFSC but it is not provided in the figures. 3DFSC is one of most important plots in cryo-EM analysis to verify directional resolution and density isotropy. <br /> In Figure 4A, the authors said that the homology model generated from TG3 was an excellent fit for the map under Ca2+ conditions. Considering Figure S2, the fit looks excellent certainly. But it was just a visual evaluation, and quantitative scores to validate the degree of fitness like the Q score should be provided.<br /> In the text mentioning Figure 4B, the authors said “a homology model of TG2 bound to Ca2+ at the three conserved binding sites and found that it was in good agreement with the cryo-EM map”. However, this sentence does not seem to match the figure because Figure 4B shows the calcium-binding sites and the related residues in the model, not including the cryo-EM map. Therefore, we suggest that Figure S2 which shows how the calcium-binding sites fit the map is included in Figure 4 instead of Figure 4B.<br /> In Figure 4, SAXS and Cryo-EM under Ca2+ conditions showed different conformations based on each protein concentration. Do you have information about the concentration of TG2 in human cells and how this relates to regulation? <br /> The authors explained that TG2 R580K mutant forms higher-order oligomers at lower Ca2+ concentrations compared to WT TG2 from Figure 4. However, at this stage, proof that WT TG2 forms higher-order oligomers seems to be only the I(0) value of the red SAXS profile in Figure 4C. In addition, since the profile is well-fitted to the calculated open-dimer profile, readers might not notice the increased I(0) value. Figure S7B looks like a more direct proof of WT TG2 higher-order oligomers under Ca2+ conditions. Therefore, we suggest that Figure S7B is included in Figure 4 or is mentioned in the text related to Figure 4.<br /> In general, SAXS has technical limitations in confirming the presence of oligomeric species due to the possibility of non-specific aggregates, precipitates, and buffer components scattering at low q values. Addressing these sources of low q scattering either through explicit mention in the text or furnishing more direct evidence of the TG2 oligomers may enhance the strength of the claims.<br /> The authors mention that using 3 μM TG2 in cryo-EM made it possible to capture monomeric TG2. The SAXS experiments required higher concentrations (25 μM) for sufficient signal. Given the importance of TG2 dimers in this study, the authors might consider measuring the affinity constant for self-association to confirm that the stoichiometry (homodimer/monomer) in the different experiments is indeed what they expect based on the solution behavior of TG2.<br /> Can the authors explain the significance of the differences in fluorescence emission at each arrow point for no drug vs. LM11 treatment in the BODIPY-GTP binding assays in Figure 5A?<br /> Some discussion regarding the limitations in using two cell lines that possibly differ in expression levels of genes other than TG2, membrane permeability, metabolic activities etc. to assess LM11 potency, can align the conclusions more closely with the data.

Minor points:

Table S2 is not mentioned in the text. <br /> In Figure 3D, it is better to describe what concentration of GTP the experimental curves have clearly. Certainly, we can read those based on the values of Rg in Figures 2 and 3B but that’s a bit unfriendly.<br /> There seems to be a typo in the text of Figure 4C. “yellow, see Figure S3C” looks like the correct text because Figure 3C does not include SAXS profiles.<br /> Figures 2 and 3 can be combined to make it easier for the reader to compare between TG2 WT and R580K.<br /> The term “saturated conformational state” in the legend for Figure 3B is not meaningful.<br /> Are the % cell viability data for LM11 and TTGM 5826 normalized to vehicle control?

Reviewed by Hiroki Yamamura, Rishav Mitra, and James Fraser (UCSF)

On 2024-04-18 19:30:24, user KL wrote:

Very interesting paper, however I have a concern about Fig 2B. Specifically, your Male/Female assignments are inconsistent between the two plots - see the line of 4 people above the alpha/beta main cluster? In the left panel they are F, M, F, M, while in the right panel they are all female. This is true of other date points between the two panels, just providing one example. The left plot also does not appear to contain any white males or females, despite them being included in the legend, though I think I see the X-with-box symbol in the right panel in a few places (pink, purple, orange).

On 2024-04-18 16:42:47, user Aaron Puri wrote:

This has been published:<br /> https://academic.oup.com/ismej/advance-article/doi/10.1093/ismejo/wrae060/7646178?login=false

On 2024-04-18 14:25:15, user Leonardo Martins wrote:

It is great that you recapitulate our findings https://doi.org/10.1093/nar... . It is not so great that you haven't seen our paper yet, but please take a look at it.

On 2024-04-18 11:32:12, user Karyn Esser wrote:

I want to note that due to some computer problems in the lab we have lost a significant amount of the real time bioiluminescence recordings for the PER2:LUC tissue clock results reported. As such, we are not confident moving forward with peer review. Thus, we will be repeating and expanding this study moving forward. Karyn Esser

On 2024-04-18 00:42:18, user CommunityScientist wrote:

Work would benefit greatly from a mutation of other zinc fingers and data to suggest there is no DNA binding occurring. Furthermore, I am alarmed by figure 3 as this is not the correct way to report replicates in SMT experiments, arising doubt surrounding the analysis and significance. Figure 4E needs explanation as to why the values overlap yet are shown to be statistically significant. I do not believe the correct statistical tests were used.

On 2024-04-17 20:01:01, user Christopher Hart wrote:

It's great that the authors have put together a manuscript that aims to generate data on an important but often ignored question in cell biology on a neglected clade. I have a few questions regarding their methods listed below, where I could not replicate or understand their choices.

If I'm reading the methods correctly the authors have used two sets of primers to amplify two transcripts from cDNA: PAPYR_7259 (P7) and PAPYR_8006 (P8). They then clone these into an expression vector to generate antibodies that they've used for some IF and a pulldown. <br /> Importantly PAPYR_7259 has several strong interpro domains corresponding to subunits of ubiquitin activating E1 domains that each hit 4-5 different genes, so the antibody specificity will be dependent on which epitopes were available in the expression vector and their antigenicity. In an effort to understand whether the authors have used a truncated protein with just the SFA domains or the whole protein including the ubiquitin interacting domains I looked for primer binding sites within each gene. I BLASTed the primers against the genome, and found binding sites for the PAPYR_7259 Fwd/Rev primers that would amplify a region from 232645 - 233620 within the genome that is near the start/stop codons of 232642 - 240693. This suggests that they are taking nearly the whole protein including the N-term Ubiquitin activating E1 domains, could the authors confirm this is the case? As for PAPYR_8006 the Fwd primer bind in the region of 147480, however I was entirely unable to find a hit by BLASTn for the listed reverse primer (GCTAGCGGCCGCATGGGTGATGACGTGGAGGCCGTCCTG) against any Paratrimastix genome in Genbank. It’s possible that the authors are using their own genome sequence, however this should be included as data within the paper and should be acknowledged in their methods. It may also be that the authors are using this primer to do a Gibson assembly so we would expect it to be less homologous to the paratrimastix pyriformis genome, however this is not explained in the methods either, and the 6-8bp 5’ non-homologous regions of the other primers suggests to me that this is simple enzyme cloning, and can all be located by BLAST. Could the authors please double check that the sequence in the text is correct and double check where it binds, and which genome version they’re using?<br /> The authors also mention that they validated their antibodies by Western Blot, however the data is not present in the paper, it would be excellent to include that data, even if it’s just as a supplemental figure. <br /> The authors then conduct expansion and Immunofluorescent microscopy to localize the SFAs within this unique organism. While expansion produces very pretty images, the authors do not include non-expanded cells, and given the difficulties of working with expanded cells and how little is known about Paratrimastix sp. it is important that the authors also show any data that they might have to show the same binding pattern is similar in non-expanded cells. I know too in our lab we've had difficulties getting DAPI/hoescht to work well with expansion but no issues with regular IF, so that inclusion would be great. Notably too the authors do not show P7 IF staining, it would be good to look at the localization of both SFAs, not just one, and this may be simpler to do in non-expanded cells.

On 2024-04-16 19:58:42, user Marie-Alda Gilles-Gonzalez wrote:

The much higher affinities you report do not agree with the Wayne model of Mtb. How did you purify your DosT and DosS proteins? Since you are impugning our work, and it does matter very much how the proteins are purified, you should provide information on this. Were your DosT and DosS fusion proteins? Were they tagged? If so, how and where?

On 2024-04-15 15:15:55, user Vojtěch Čermák wrote:

This work has been published in Frontier in Plant Science: https://doi.org/10.3389/fpl...

On 2024-04-15 09:14:42, user Marcos Suárez wrote:

Published in PeerJ DOI: 10.7717/peerj.16028

On 2024-04-15 08:03:17, user Kazuhito Tabata wrote:

Your reports have been very interesting. The improved efficiency of PURE synthesis is an important finding for the future of synthetic biology and would be an interesting topic for the field of materials production. In this paper you discuss the effect of molecular crowders, but we also tested the effect of TMAO and betaine.

https://pubs.acs.org/doi/fu...

We also tested the effect of TMAO and betaine and found that 100mM or 1M was not as effective, but 0.4M improved protein synthesis by about 2 times. I am very happy to see that the results are the same as what you tested. I am also very interested to see if the conditions we found will further improve your result.

On 2024-04-12 19:22:42, user Xiaohui Zha wrote:

It is now published by eLife:

https://elifesciences.org/a...

Xiaohui Zha

On 2024-04-12 18:10:47, user Luis E. Gimenez wrote:

Regarding Figure 1B, it is incorrect to calculate arithmetic averages for EC50 values, even more so to show scatter measures on an arithmetic scale, given that EC50 values do not typically follow a Gaussian distribution. Instead, The authors should show pEC50 values and apply one-way ANOVA to the transformed data.

On 2024-04-12 16:31:13, user Hisashi Tanaka wrote:

The manuscript has been published online in Nucleic Acids Research Cancer.<br /> https://academic.oup.com/na...

On 2024-04-12 16:25:56, user Lori Passmore wrote:

COMMENTS FROM PASSMORE LAB JOURNAL CLUB:

In this manuscript the authors show how LEA proteins can improve protein behaviour in cryoEM. This has an advantage over using detergents such as CHAPSO, because the target protein concentration can remain lower. It seems like this would be a straightforward method to implement for challenging specimens and therefore should be of broad interest.

We would find it helpful if the authors could provide more methodological detail in the manuscript, especially given it is a methods-based paper. For example, when was LEA added to the samples? What concentration was the stock solution of LEA? Is glycerol necessary for LEA protein stability?

The manuscript would be strengthened by investigation into the mechanistic basis of how LEA proteins improve particle quality. The authors hypothesize that LEA coats the air-water interface and could further investigate this. The orientation bias of the samples in this study suggests that the sample proteins are still interacting with an interface. Tomography could help explain these.

It would also be interesting to know if the authors attempted to reconstruct crosslinked PRC2 in the absence of LEA (as a control, instead of comparing to other laboratories' work).

On 2024-04-12 15:15:07, user Alaina wrote:

I really enjoyed reading this paper. Very exciting results! I am wondering how the results differed between the cultured genomes and the MAGs? MAGs only represent a population average of a genome, lacking that individual-level genome variability which defenses tend to exhibit. Were the results different between MAGs and cultured genomes? Also if available, I'd recommend including SAGs as well to recover that variability / microdiversity.

On 2024-04-12 14:54:46, user Rumiana Dimova wrote:

The manuscript was published: https://doi.org/10.1002/adv...<br /> A. Mangiarotti, M. Aleksanyan, M. Siri, T.-W. Sun, R. Lipowsky, R. Dimova, Photoswitchable Endocytosis of Biomolecular Condensates in Giant Vesicles. Adv. Sci. 2024, 2309864.

On 2024-04-12 07:33:41, user Priyanka Sharma wrote:

https://www.cell.com/iscien...

On 2024-04-10 23:38:51, user Minho Song wrote:

This manuscript is now accepted in IEEE TUFFC journal, as linked below.<br /> https://doi.org/10.1109/tuf...

On 2024-04-10 17:36:11, user VN wrote:

This pre-print has been accepted for publication in Molecular Ecology (https://onlinelibrary.wiley....

On 2024-04-10 13:58:54, user Shelly Peyton wrote:

I teach a professional development course for graduate<br /> students, and we reviewed your paper last week. We loved it! As part of the class, we are providing comments as reviewers, which I've compiled here, and we hope you find them useful!

Introduction and Abstract:<br /> Strengths:<br /> - good summary of current work in the field, well motivated

Potential improvement:<br /> -Could be more clear to introduce cell migration first then explain the impact of the ECM on these processes which is a smoother lead in to the research question. Right now it jumps from ECM to migration back to ECM and reads as choppy and disjointed.